Update README.md

README.md

@@ -3,6 +3,7 @@ language:
 - en
 license: apache-2.0
 base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+base_model_relation: finetune
 tags:
 - code
 - coding
@@ -13,6 +14,10 @@ tags:
 - complexity-analysis
 - qwen2.5
 - fine-tuned
+- vanta-research
+- vanta-research-entities
+- vanta-research-code-models
+- wraith
 model-index:
 - name: wraith-coder-7b
   results:
@@ -26,6 +31,24 @@ model-index:
     - type: coverage
       value: 60
       name: Complexity Analysis Coverage
+library_name: transformers
+---
+
+<div align="center">
+
+![vanta_trimmed](https://cdn-uploads.huggingface.co/production/uploads/686c460ba3fc457ad14ab6f8/hcGtMtCIizEZG_OuCvfac.png)
+  
+  <h1>VANTA Research</h1>
+    
+  <p><strong>Independent AI safety research lab specializing in cognitive fit, alignment, and human-AI collaboration</strong></p>
+  
+  <p>
+    <a href="https://unmodeledtyler.com"><img src="https://img.shields.io/badge/Website-unmodeledtyler.com-yellow" alt="Website"/></a>
+    <a href="https://x.com/vanta_research"><img src="https://img.shields.io/badge/@vanta_research-1DA1F2?logo=x" alt="X"/></a>
+    <a href="https://github.com/vanta-research"><img src="https://img.shields.io/badge/GitHub-vanta--research-181717?logo=github" alt="GitHub"/></a>
+  </p>
+</div>
+
 ---
 
 # Wraith Coder 7B
@@ -34,7 +57,7 @@ Wraith Coder 7B is a specialized code generation model fine-tuned from Qwen2.5-C
 
 ## Model Description
 
-**Developed by:** Vanta Research  
+**Developed by:** VANTA Research  
 **Base Model:** Qwen/Qwen2.5-Coder-7B-Instruct  
 **Model Type:** Causal Language Model  
 **Language(s):** English  
@@ -56,26 +79,27 @@ Wraith Coder 7B is a specialized code generation model fine-tuned from Qwen2.5-C
 
 Wraith Coder 7B was developed through three iterations of progressive capability enhancement:
 
-**Iteration 1: Personality Establishment (4,256 examples)**
+**Iteration 1: Personality Establishment (~4,250 examples)**
+- Same personality examples used on Wraith 8B from the VANTA Research Entity Series
 - Identity formation and communication style
 - Logical reasoning patterns
 - Technical terminology usage
 - Foundation for signal-dense communication
 
-**Iteration 2: Coding Restoration (5,500 examples)**
-- 2,040 conversational coding examples
-- 2,040 computer science fundamentals
-- 920 mathematical reasoning problems
-- 200 identity reinforcement examples
-- 300 technical communication patterns
-
-**Iteration 3: Advanced Capabilities (4,488 examples)**
-- 1,007 architectural design patterns
-- 1,041 algorithm design and analysis
-- 1,064 debugging techniques
-- 1,026 systems programming concepts
-- 150 identity anchors
-- 200 communication pattern reinforcement
+**Iteration 2: Coding Restoration/Enhancement (~5,500 examples)**
+- Conversational coding examples
+- Computer science fundamentals
+- Mathematical reasoning problems
+- Identity reinforcement examples
+- Technical communication patterns
+
+**Iteration 3: Advanced Capabilities (~4,450 examples)**
+- Architectural design patterns
+- Algorithm design and analysis
+- Debugging techniques
+- Systems programming concepts
+- Identity anchors
+- Communication pattern reinforcement
 
 ### Training Configuration
 
@@ -271,11 +295,7 @@ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(response)
 ```
 
-## Model Card Authors
-
-Vanta Research
-
-## Model Card Contact
+## Contact
 
 For questions or issues regarding this model, please open an issue in the model repository.
 
@@ -285,7 +305,7 @@ If you use this model in your research or applications, please cite:
 
 ```bibtex
 @misc{wraith-coder-7b,
-  author = {Vanta Research},
+  author = {VANTA Research},
   title = {Wraith Coder 7B: Signal-Dense Code Generation through Iterative Fine-Tuning},
   year = {2025},
   publisher = {Hugging Face},
@@ -295,7 +315,7 @@ If you use this model in your research or applications, please cite:
 
 ## Acknowledgments
 
-This model builds upon Qwen2.5-Coder-7B-Instruct developed by Alibaba Cloud. We acknowledge their contribution to open-source language model research.
+This model builds upon Qwen2.5-Coder-7B-Instruct developed by Alibaba Cloud. We acknowledge their contribution to open-source language model research. Thanks to Unsloth for providing an easy-to-use training framework.
 
 ## Version History
 
@@ -303,3 +323,6 @@ This model builds upon Qwen2.5-Coder-7B-Instruct developed by Alibaba Cloud. We
   - 62.6% response reduction while maintaining correctness
   - 60% complexity analysis coverage across 20-question benchmark
   - Production-ready for senior engineering applications
+
+---
+*Proudly developed in Portland, Oregon by VANTA Research*